Phase transition in the scalar noise model of collective motion in three dimensions

We consider disorder-order phase transitions in the three-dimensional version of the scalar noise model (SNM) of flocking. Our results are analogous to those found for the two-dimensional case. For small velocity (v <= 0.1) a continuous, second-order phase transition is observable, with the diffusion of nearby particles being isotropic. By increasing the particle velocities the phase transition changes to first order, and the diffusion becomes anisotropic. The first-order transition in the latter case is probably caused by the interplay between anisotropic diffusion and periodic boundary conditions, leading to a boundary condition dependent symmetry breaking of the solutions.Comment: 7 pages, 6 figures; submitted to EPJ on 17 of April, 200

Neural networks versus Logistic regression for 30 days all-cause readmission prediction

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1)explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2)to examine the additive value of patients' hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95%CI, 0.640-0.646). We conclude that data from patient timelines improve 30 day readmission prediction for neural network-based models, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets

Mining Images in Biomedical Publications: Detection and Analysis of Gel Diagrams

Authors of biomedical publications use gel images to report experimental results such as protein-protein interactions or protein expressions under different conditions. Gel images offer a concise way to communicate such findings, not all of which need to be explicitly discussed in the article text. This fact together with the abundance of gel images and their shared common patterns makes them prime candidates for automated image mining and parsing. We introduce an approach for the detection of gel images, and present a workflow to analyze them. We are able to detect gel segments and panels at high accuracy, and present preliminary results for the identification of gene names in these images. While we cannot provide a complete solution at this point, we present evidence that this kind of image mining is feasible.Comment: arXiv admin note: substantial text overlap with arXiv:1209.148

A bio-motivated vision system and artificial neural network for autonomous UAV obstacle avoidance

Unmanned aerial vehicles (UAVs) are becoming more and more common. They show excellent potential for multiple types of autonomous work, although they must achieve these tasks safely. For flight safety, it must be assured that the UAV will avoid collision with any objects in its flight path during autonomous operations. Computer vision and artificial neural networks are effective in many applications. However, biological vision systems and the brain areas responsible for visual processing may hold solutions capable of acquiring information effectively. We propose a novel system, which performs visual cue extraction with algorithms based on the structure and functionality of the retina and the visual cortex of the mammalian visual system, and a convolutional neural network processing data to detect a predefined obstacle using the onboard camera of the UAV. We also examined the effect of preprocessing on calculation time and recognition effectiveness

Relevance of CYP2C9 Function in Valproate Therapy

Genetic polymorphisms of drug metabolizing enzymes can substantially modify the pharmacokinetics of a drug and eventually its efficacy or toxicity; however, inferring a patient's drug metabolizing capacity merely from his or her genotype can lead to false prediction. Non-genetic host factors (age, sex, disease states) and environmental factors (nutrition, co-medication) can transiently alter the enzyme expression and activities resulting in genotype-phenotype mismatch. Although valproic acid is a well-tolerated anticonvulsant, pediatric patients are particularly vulnerable to valproate injury that can be partly attributed to the age-related differences in metabolic pathways. CYP2C9 mediated oxidation of valproate, which is the minor metabolic pathway in adults, appears to become the principal route in children. Genetic and non-genetic variations in CYP2C9 activity can result in significant inter- and intra-individual differences in valproate pharmacokinetics and valproate induced adverse reactions. The loss-of-function alleles, CYP2C9*2 or CYP2C9*3, display significant reduction in valproate metabolism in children; furthermore, low CYP2C9 expression in patients with CYP2C9*1/*1 genotype also leads to a decrease in valproate metabolizing capacity. Due to phenoconversion, the homozygous wild genotype, expected to be translated to CYP2C9 enzyme with normal activity, is transiently switched into poor (or extensive) metabolizer phenotype. Novel strategy for valproate therapy adjusted to CYP2C9-status (CYP2C9 genotype and CYP2C9 expression) is strongly recommended in childhood. The early knowledge of pediatric patients' CYP2C9-status facilitates the optimization of valproate dosing which contributes to the avoidance of misdosing induced adverse reactions, such as abnormal blood levels of ammonia and alkaline phosphatase, and improves the safety of children's anticonvulsant therapy. 

The mitogen-activated protein kinome from Anopheles gambiae: identification, phylogeny and functional characterization of the ERK, JNK and p38 MAP kinases

<p>Abstract</p> <p>Background</p> <p><it>Anopheles gambiae </it>is the primary mosquito vector of human malaria parasites in sub-Saharan Africa. To date, three innate immune signaling pathways, including the nuclear factor (NF)-kappaB-dependent Toll and immune deficient (IMD) pathways and the Janus kinase/signal transducers and activators of transcription (Jak-STAT) pathway, have been extensively characterized in <it>An. gambiae</it>. However, in addition to NF-kappaB-dependent signaling, three mitogen-activated protein kinase (MAPK) pathways regulated by JNK, ERK and p38 MAPK are critical mediators of innate immunity in other invertebrates and in mammals. Our understanding of the roles of the MAPK signaling cascades in anopheline innate immunity is limited, so identification of the encoded complement of these proteins, their upstream activators, and phosphorylation profiles in response to relevant immune signals was warranted.</p> <p>Results</p> <p>In this study, we present the orthologs and phylogeny of 17 <it>An. gambiae </it>MAPKs, two of which were previously unknown and two others that were incompletely annotated. We also provide detailed temporal activation profiles for ERK, JNK, and p38 MAPK in <it>An. gambiae </it>cells <it>in vitro </it>to immune signals that are relevant to malaria parasite infection (human insulin, human transforming growth factor-beta1, hydrogen peroxide) and to bacterial lipopolysaccharide. These activation profiles and possible upstream regulatory pathways are interpreted in light of known MAPK signaling cascades.</p> <p>Conclusions</p> <p>The establishment of a MAPK "road map" based on the most advanced mosquito genome annotation can accelerate our understanding of host-pathogen interactions and broader physiology of <it>An. gambiae </it>and other mosquito species. Further, future efforts to develop predictive models of anopheline cell signaling responses, based on iterative construction and refinement of data-based and literature-based knowledge of the MAP kinase cascades and other networked pathways will facilitate identification of the "master signaling regulators" in biomedically important mosquito species.</p

De novo implantation vs. upgrade cardiac resynchronization therapy: a systematic review and meta-analysis

Patients with conventional pacemakers or implanted defibrillators are often considered for cardiac resynchronization therapy (CRT). Our aim was to summarize the available evidences regarding the clinical benefits of upgrade procedures. A systematic literature search was performed from studies published between 2006 and 2017 in order to compare the outcome of CRT upgrade vs. de novo implantations. Outcome data on all-cause mortality, heart failure events, New York Heart Association (NYHA) Class, QRS narrowing and echocardiographic parameters were analysed. A total of 16 reports were analysed comprising 489,568 CRT recipients, of whom 468,205 patients underwent de novo and 21,363 upgrade procedures. All-cause mortality was similar after CRT upgrade compared to de novo implantations (RR 1.19, 95% CI 0.88-1.60, p = 0.27). The risk of heart failure was also similar in both groups (RR 0.96, 95% CI 0.70-1.32, p = 0.81). There was no significant difference in clinical response after CRT upgrade compared to de novo implantations in terms of improvement in left ventricular ejection fraction (DeltaEF de novo - 6.85% vs. upgrade - 9.35%; p = 0.235), NYHA class (DeltaNYHA de novo - 0.74 vs. upgrade - 0.70; p = 0.737) and QRS narrowing (DeltaQRS de novo - 9.6 ms vs. upgrade - 29.5 ms; p = 0.485). Our systematic review and meta-analysis of currently available studies reports that CRT upgrade is associated with similar risk for all-cause mortality compared to de novo resynchronization therapy. Benefits on reverse remodelling and functional capacity improved similarly in both groups suggesting that CRT upgrade may be safely and effectively offered in routine practice. CLINICAL TRIAL REGISTRATION: Prospero Database-CRD42016043747